RED-GENE: An Evolutionary Game Theoretic Approach to Adaptive Data Stream Classification
- Submitting institution
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Birmingham City University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11Z_OP_D0044
- Type
- D - Journal article
- DOI
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10.1109/ACCESS.2019.2954993
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 173944
- Volume
- 7
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2019
- URL
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https://ieeexplore.ieee.org/document/8908673
- Supplementary information
-
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
-
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The study makes significant contributions to both evolutionary game theory (as demonstrated in https://doi.org/10.3390/g9020031) and processing evolving data streams (as acknowledged by https://doi.org/10.1109/ACCESS.2020.3046132 and https://doi.org/10.1109/ACCESS.2020.2965766). The proposed method can be applied to many modern problem domains such as Internet of Things, spam filtering, stock market prediction and fraud detection.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -